Article: Neuromorphic-P
2023 Volume 17, Page(s) 1144301
Abstract: Edge devices equipped with computer vision must deal with vast amounts of sensory data with limited computing resources. Hence, researchers have been exploring different energy-efficient solutions such as near-sensor, in-sensor, and in-pixel processing, ... ...
| Abstract | Edge devices equipped with computer vision must deal with vast amounts of sensory data with limited computing resources. Hence, researchers have been exploring different energy-efficient solutions such as near-sensor, in-sensor, and in-pixel processing, bringing the computation closer to the sensor. In particular, in-pixel processing embeds the computation capabilities inside the pixel array and achieves high energy efficiency by generating low-level features instead of the raw data stream from CMOS image sensors. Many different in-pixel processing techniques and approaches have been demonstrated on conventional frame-based CMOS imagers; however, the processing-in-pixel approach for neuromorphic vision sensors has not been explored so far. In this work, for the first time, we propose an asynchronous non-von-Neumann analog processing-in-pixel paradigm to perform convolution operations by integrating |
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| Language | English |
| Publishing date | 2023-05-04 |
| Publishing country | Switzerland |
| Document type | Journal Article |
| ZDB-ID | 2452979-5 |
| ISSN | 1662-5196 |
| ISSN | 1662-5196 |
| DOI | 10.3389/fninf.2023.1144301 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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